Neural Splines: Exploiting Parallelism for Function Approximation Using Modular Neural Networks

We introduce the Neural Spline, that is a mathematical model built by combining a neural network and an associated Obreshkov polynomial. The neural spline has finite support and can be used as the basic element in constructing continuous modular neural-based models. These models are suitable for function approximation in partitioned domains and are also amenable to efficient parallel or distributed implementation. Experimental results are presented for test problems in one and two dimensions which illustrate the effectiveness of the proposed function approximation scheme.